PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data

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ID: 319554
2026
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Abstract
Abstract Motivation Graphical models have been widely used in bioinformatics to infer the conditional dependence structure among random variables, but traditional Gaussian graphical models (GGMs) are suboptimal for single-cell RNA sequencing (scRNA-seq) due to dropout events and distributional mismatch. Moreover, most existing methods estimate networks under a single condition, limiting their utility in multi-condition studies. Results We propose PLNFGL (Poisson Log-Normal Fused Graphical Lasso), a joint network estimation framework for scRNA-seq data. PLNFGL uses a multivariate Poisson log-normal model to accommodate dropout effects and estimates the covariance via moment methods. A joint graphical model is then employed to infer condition-specific precision matrices. Simulations show improved estimation accuracy. Applications to scRNA-seq data of Alzheimer’s disease and spatial transcriptomics of lung cancer reveal cell-type-specific interaction networks. Edge set enrichment enables pathway analysis, validating known interactions and highlighting novel disease-related targets. This work provides a powerful tool for the integrative analysis of scRNA-seq data. Availability and Implementation The R implementation of PLNFGL is available at https://github.com/jijiadong/PLNFGL, and an archival version is available on Zenodo at https://doi.org/10.5281/zenodo.20744172. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7167224865 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wenli Zhai, Dan Zhou, Zhongshang Yuan, Jiadong Ji
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag485
URL
https://doi.org/10.1093/bioinformatics/btag485
Keywords Keywords not found

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